TADPOLE_Survival

1 TADPOLE Univariate Options Analysis

1.0.1 Loading the libraries

library("FRESA.CAD")
library(readxl)
library("whitening")
library("fpc")
library("robust")
op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('table.split.table', 400)
pander::panderOptions('keep.trailing.zeros',TRUE)
TrainFraction <- 0.50;

dataLoad = FALSE

1.1 The data set

TADPOLE_D1_D2 <- read.csv("~/GitHub/BSWiMS/Data/TADPOLE/TADPOLE_D1_D2.csv")
TADPOLE_D1_D2_Dict <- read.csv("~/GitHub/BSWiMS/Data/TADPOLE/TADPOLE_D1_D2_Dict.csv")
TADPOLE_D1_D2_Dict_LR <- as.data.frame(read_excel("~/GitHub/BSWiMS/Data/TADPOLE/TADPOLE_D1_D2_Dict_LR.xlsx",sheet = "LeftRightFeatures"))


rownames(TADPOLE_D1_D2_Dict) <- TADPOLE_D1_D2_Dict$FLDNAME

1.2 Conditioning the data

# mm3 to mm
isVolume <- c("Ventricles","Hippocampus","WholeBrain","Entorhinal","Fusiform","MidTemp","ICV",
              TADPOLE_D1_D2_Dict$FLDNAME[str_detect(TADPOLE_D1_D2_Dict$TEXT,"Volume")]
              )


#TADPOLE_D1_D2[,isVolume] <- apply(TADPOLE_D1_D2[,isVolume],2,'^',(1/3))
TADPOLE_D1_D2[,isVolume] <- TADPOLE_D1_D2[,isVolume]^(1/3)

# mm2 to mm
isArea <- TADPOLE_D1_D2_Dict$FLDNAME[str_detect(TADPOLE_D1_D2_Dict$TEXT,"Area")]
TADPOLE_D1_D2[,isArea] <- sqrt(TADPOLE_D1_D2[,isArea])

# Get only cross sectional measurements
FreeSurfersetCross <- str_detect(colnames(TADPOLE_D1_D2),"UCSFFSX")

# The subset of baseline measurements
baselineTadpole <- subset(TADPOLE_D1_D2,VISCODE=="bl")
table(baselineTadpole$DX)

                   Dementia Dementia to MCI             MCI MCI to Dementia 
          7             336               1             864               5 
  MCI to NL              NL       NL to MCI 
          2             521               1 

table(baselineTadpole$DX_bl)

AD CN EMCI LMCI SMC 342 417 310 562 106

rownames(baselineTadpole) <- baselineTadpole$PTID


validBaselineTadpole <- cbind(DX=baselineTadpole$DX_bl,
                                 AGE=baselineTadpole$AGE,
                                 Gender=1*(baselineTadpole$PTGENDER=="Female"),
                                 ADAS11=baselineTadpole$ADAS11,
                                 ADAS13=baselineTadpole$ADAS13,
                                 MMSE=baselineTadpole$MMSE,
                                 RAVLT_immediate=baselineTadpole$RAVLT_immediate,
                                 RAVLT_learning=baselineTadpole$RAVLT_learning,
                                 RAVLT_forgetting=baselineTadpole$RAVLT_forgetting,
                                 RAVLT_perc_forgetting=baselineTadpole$RAVLT_perc_forgetting,
                                 FAQ=baselineTadpole$FAQ,
                                 Ventricles=baselineTadpole$Ventricles,
                                 Hippocampus=baselineTadpole$Hippocampus,
                                 WholeBrain=baselineTadpole$WholeBrain,
                                 Entorhinal=baselineTadpole$Entorhinal,
                                 Fusiform=baselineTadpole$Fusiform,
                                 MidTemp=baselineTadpole$MidTemp,
                                 ICV=baselineTadpole$ICV,
                                 baselineTadpole[,FreeSurfersetCross])


LeftFields <- TADPOLE_D1_D2_Dict_LR$LFN
names(LeftFields) <- LeftFields
LeftFields <- LeftFields[LeftFields %in% colnames(validBaselineTadpole)]
RightFields <- TADPOLE_D1_D2_Dict_LR$RFN
names(RightFields) <- RightFields
RightFields <- RightFields[RightFields %in% colnames(validBaselineTadpole)]

## Normalize to ICV
validBaselineTadpole$Ventricles=validBaselineTadpole$Ventricles/validBaselineTadpole$ICV
validBaselineTadpole$Hippocampus=validBaselineTadpole$Hippocampus/validBaselineTadpole$ICV
validBaselineTadpole$WholeBrain=validBaselineTadpole$WholeBrain/validBaselineTadpole$ICV
validBaselineTadpole$Entorhinal=validBaselineTadpole$Entorhinal/validBaselineTadpole$ICV
validBaselineTadpole$Fusiform=validBaselineTadpole$Fusiform/validBaselineTadpole$ICV
validBaselineTadpole$MidTemp=validBaselineTadpole$MidTemp/validBaselineTadpole$ICV

leftData <- validBaselineTadpole[,LeftFields]/validBaselineTadpole$ICV
RightData <- validBaselineTadpole[,RightFields]/validBaselineTadpole$ICV

## get mean and relative difference 
meanLeftRight <- (leftData + RightData)/2
difLeftRight <- abs(leftData - RightData)
reldifLeftRight <- difLeftRight/meanLeftRight
colnames(meanLeftRight) <- paste("M",colnames(meanLeftRight),sep="_")
colnames(difLeftRight) <- paste("D",colnames(difLeftRight),sep="_")
colnames(reldifLeftRight) <- paste("RD",colnames(reldifLeftRight),sep="_")


validBaselineTadpole <- validBaselineTadpole[,!(colnames(validBaselineTadpole) %in% 
                                               c(LeftFields,RightFields))]
#validBaselineTadpole <- cbind(validBaselineTadpole,meanLeftRight,difLeftRight,reldifLeftRight)
validBaselineTadpole <- cbind(validBaselineTadpole,meanLeftRight,difLeftRight)

## Remove columns with too many NA more than %15 of NA
nacount <- apply(is.na(validBaselineTadpole),2,sum)/nrow(validBaselineTadpole) < 0.15
diagnose <- validBaselineTadpole$DX
pander::pander(table(diagnose))

AD	CN	EMCI	LMCI	SMC
342	417	310	562	106

validBaselineTadpole <- validBaselineTadpole[,nacount]
## Remove character columns
ischar <- sapply(validBaselineTadpole,class) == "character"
validBaselineTadpole <- validBaselineTadpole[,!ischar]
## Place back diagnose
validBaselineTadpole$DX <- diagnose


validBaselineTadpole <- validBaselineTadpole[complete.cases(validBaselineTadpole),]
ischar <- sapply(validBaselineTadpole,class) == "character"
validBaselineTadpole[,!ischar] <- sapply(validBaselineTadpole[,!ischar],as.numeric)

colnames(validBaselineTadpole) <- str_remove_all(colnames(validBaselineTadpole),"_UCSFFSX_11_02_15_UCSFFSX51_08_01_16")
colnames(validBaselineTadpole) <- str_replace_all(colnames(validBaselineTadpole)," ","_")
validBaselineTadpole$LONISID <- NULL
validBaselineTadpole$IMAGEUID <- NULL
validBaselineTadpole$LONIUID <- NULL

diagnose <- as.character(validBaselineTadpole$DX)
validBaselineTadpole$DX <- diagnose
pander::pander(table(validBaselineTadpole$DX))

AD	CN	EMCI	LMCI	SMC
245	359	272	444	93

validBaselineTadpole[validBaselineTadpole$DX %in% c("EMCI","LMCI"),"DX"] <- "MCI" 
validBaselineTadpole[validBaselineTadpole$DX %in% c("CN","SMC"),"DX"] <- "NL" 

pander::pander(table(validBaselineTadpole$DX))

AD	MCI	NL
245	716	452

1.3 Get the Time To Event on MCI Subjects

subjectsID <- rownames(validBaselineTadpole)
visitsID <- unique(TADPOLE_D1_D2$VISCODE)
baseDx <- TADPOLE_D1_D2[TADPOLE_D1_D2$VISCODE=="bl",c("PTID","DX","EXAMDATE")]
rownames(baseDx) <- baseDx$PTID 
baseDx <- baseDx[subjectsID,]
lastDx <- baseDx
toDementia <- baseDx
table(lastDx$DX)

   Dementia Dementia to MCI             MCI MCI to Dementia       MCI to NL 
        244               1             711               2               2 
         NL       NL to MCI 
        452               1 

hasDementia <- lastDx$PTID[str_detect(lastDx$DX,"Dementia")]


for (vid in visitsID)
{
  DxValue <- TADPOLE_D1_D2[TADPOLE_D1_D2$VISCODE==vid,c("PTID","DX","EXAMDATE")]
  rownames(DxValue) <- DxValue$PTID 
  DxValue <- DxValue[DxValue$PTID %in% subjectsID,]
  noDX <- DxValue$PTID[nchar(DxValue$DX) < 1]
  print(length(noDX))
  DxValue[noDX,] <- lastDx[noDX,]
  inLast <- lastDx$PTID[lastDx$PTID %in% DxValue$PTID]
  print(length(inLast))
  lastDx[inLast,] <- DxValue[inLast,]
  noDementia <- !(toDementia$PTID %in% hasDementia)
  toDementia[noDementia,] <- lastDx[noDementia,]
  hasDementia <- unique(c(hasDementia,lastDx$PTID[str_detect(lastDx$DX,"Dementia")]))
}

[1] 0 [1] 1413 [1] 2 [1] 1326 [1] 6 [1] 1218 [1] 23 [1] 1095 [1] 805 [1] 1058 [1] 29 [1] 710 [1] 20 [1] 212 [1] 14 [1] 167 [1] 32 [1] 553 [1] 25 [1] 298 [1] 18 [1] 130 [1] 667 [1] 667 [1] 112 [1] 112 [1] 176 [1] 176 [1] 177 [1] 177 [1] 625 [1] 625 [1] 251 [1] 251 [1] 159 [1] 159 [1] 7 [1] 7 [1] 17 [1] 99 [1] 9 [1] 63 [1] 1 [1] 1

table(lastDx$DX)

   Dementia Dementia to MCI             MCI MCI to Dementia       MCI to NL 
        428               2             463              80               7 
         NL  NL to Dementia       NL to MCI 
        406               1              26 

baseMCI <-baseDx$PTID[baseDx$DX == "MCI"]
lastDementia <- lastDx$PTID[str_detect(lastDx$DX,"Dementia")]
lastDementia2 <- toDementia$PTID[str_detect(toDementia$DX,"Dementia")]
lastNL <- lastDx$PTID[str_detect(lastDx$DX,"NL")]

MCIatBaseline <- baseDx[baseMCI,]
MCIatEvent <- toDementia[baseMCI,]
MCIatLast <- lastDx[baseMCI,]

MCIconverters <- MCIatBaseline[baseMCI %in% lastDementia,]
MCI_No_converters <- MCIatBaseline[!(baseMCI %in% MCIconverters$PTID),]
MCIconverters$TimeToEvent <- (as.Date(toDementia[MCIconverters$PTID,"EXAMDATE"]) 
                                   - as.Date(MCIconverters$EXAMDATE))

sum(MCIconverters$TimeToEvent ==0)

[1] 0

MCIconverters$AtEventDX <- MCIatEvent[MCIconverters$PTID,"DX"]
MCIconverters$LastDX <- MCIatLast[MCIconverters$PTID,"DX"]

MCI_No_converters$TimeToEvent <- (as.Date(lastDx[MCI_No_converters$PTID,"EXAMDATE"]) 
                                   - as.Date(MCI_No_converters$EXAMDATE))

MCI_No_converters$LastDX <- MCIatLast[MCI_No_converters$PTID,"DX"]

MCI_No_converters <- subset(MCI_No_converters,TimeToEvent > 0)

2 Prognosis MCI to AD Conversion

2.1 the set

MCIPrognosisIDs <- c(MCIconverters$PTID,MCI_No_converters$PTID)

TADPOLECrossMRI <- validBaselineTadpole[MCIPrognosisIDs,]
table(TADPOLECrossMRI$DX)

MCI 680

TADPOLECrossMRI$DX <- NULL
TADPOLECrossMRI$status <- 1*(rownames(TADPOLECrossMRI) %in% MCIconverters$PTID)
table(TADPOLECrossMRI$status)

0 1 436 244

TADPOLECrossMRI$TimeToEvent <- numeric(nrow(TADPOLECrossMRI))
TADPOLECrossMRI[MCIconverters$PTID,"TimeToEvent"] <- MCIconverters$TimeToEvent
TADPOLECrossMRI[MCI_No_converters$PTID,"TimeToEvent"] <- MCI_No_converters$TimeToEvent



set.seed(1)
trainCases <- sample(nrow(TADPOLECrossMRI),nrow(TADPOLECrossMRI)*TrainFraction)

TADPOLE_Conv_TRAIN <- TADPOLECrossMRI[trainCases,]
TADPOLE_Conv_TEST <- TADPOLECrossMRI[-trainCases,]

pander::pander(table(TADPOLE_Conv_TRAIN$status))

0	1
218	122

pander::pander(table(TADPOLE_Conv_TEST$status))

0	1
218	122

surTimeTrain <- TADPOLE_Conv_TRAIN$TimeToEvent
surTimeTest <- TADPOLE_Conv_TEST$TimeToEvent


TADPOLE_Conv_TRAIN$TimeToEvent <- NULL
TADPOLE_Conv_TEST$TimeToEvent <- NULL

2.1.0.1 Analysis parameters

dataframe <- TADPOLE_Conv_TRAIN
dataframeTest <- TADPOLE_Conv_TEST
outcome <- "status"

2.1.1 All the run options

sig_pvalue <- 0.01
thr <- c(0.05,0.2,0.4,0.6,0.8,0.95);
method  <- c("fast","pearson","spearman");
type <- c("LM","RLM")
DeOutcome <- c("T_Blind","T_Driven")
corRank <- c(FALSE,TRUE)

#method  <- c("fast","pearson","spearman");
#type <- c("LM","RLM")

2.2 Decorrelation Analysis

if (dataLoad)
{
  load("~/GitHub/LatentBiomarkers/RMD/TADPOLE_ALL_Options.RData")
} else
{
  
  idx = 0;
  thenames <- list();
  totBaM <- NULL
  totDeM <- NULL
  toUnmatM <- NULL
  unalteredM <- NULL
  Decorrleated_FractionM<- NULL
  Base_FractionM<- NULL
  Unaltered_FractionM <- NULL
  sparcityM <- NULL
  Average_Latent_SizeM <- NULL
  SigDeM <- NULL
  La_SignificantM <- NULL
  pbKNNaucM <- NULL
  pbKNNaccM <- NULL
  
  for (DeOut in DeOutcome)
  {
    for (meth in method)
    {
      for (typ in type)
      {
        for (corran in corRank)
        {
          par(op)
          par(mfrow=c(3,2),cex=0.5)
          idx <- idx + 1;
          thenames[[idx]] <- paste(DeOut,meth,typ,corran,sep="_")
          totBa <- numeric()
          totDe <- numeric()
          toUnmat <- numeric()
          unaltered <- numeric()
          Decorrleated_Fraction<- numeric()
          Base_Fraction<- numeric()
          Unaltered_Fraction <- numeric()
          sparcity <- numeric()
          Average_Latent_Size  <- numeric()
          SigDe <- numeric()
          La_Significant <- numeric()
          pbKNNauc <- numeric()
          pbKNNacc <- numeric()
          for (thrs in thr)
          {
            print(thenames[[idx]])
            plotname <- paste(thenames[[idx]],thrs,sep="_")
            
  
            if (DeOut != "T_Driven")
            {
              DEdataframeTrain <- IDeA(dataframe,
                                                     thr=thrs,
                                                     method=meth,
                                                     type=typ,
                                                     corRank=corran,
                                                     verbose = FALSE)
            } else
            {
              DEdataframeTrain <- IDeA(dataframe,
                                                     Outcome=outcome,
                                                     thr=thrs,
                                                     method=meth,
                                                     type=typ,
                                                     corRank=corran,
                                                     verbose = FALSE)
            }
            demat <- attr(DEdataframeTrain,"UPSTM")
            DEdataframe <- predictDecorrelate(DEdataframeTrain,dataframeTest)
            
            totFe <- ncol(DEdataframe)-1
            totBa <- c(totBa,length(attr(DEdataframeTrain,"unaltered")))
            totDe <- c(totDe, sum(str_detect(colnames(DEdataframe),"La_")))
            toUn <- sum(apply(demat!=0,2,sum)==1)
            toUnmat <- c(toUnmat,toUn )
            una <-  totFe - ncol(demat) + toUn
            unaltered <- c(unaltered,una)
            Decorrleated_Fraction <- c(Decorrleated_Fraction,sum(str_detect(colnames(DEdataframe),"La_"))/totFe)
            Base_Fraction <- c(Base_Fraction,length(attr(DEdataframeTrain,"unaltered"))/totFe)
            
            Unaltered_Fraction <- c(Unaltered_Fraction,una/totFe)
            sparcity <- c(sparcity,(totFe-ncol(demat)+sum(abs(demat)!=0))/totFe/totFe)
            
            varlistDe <-  colnames(demat)[apply(demat!=0,2,sum)>1];
            varlistDe <- as.data.frame(cbind(name=varlistDe,desc=varlistDe))
            
            
            varlist_DeAll <- colnames(DEdataframe)
            varlist_DeAll <- varlist_DeAll[!(varlist_DeAll %in% c(outcome,"TimeToEvent"))]
            varlist_DeAll <- as.data.frame(cbind(name=varlist_DeAll,desc=varlist_DeAll))
            
  
            DEdataframeTime <- cbind(DEdataframeTrain,SurvTime=surTimeTest)
            
            
            pthr <- sig_pvalue/(ncol(dataframe)-1)
            
            pDe <- univariate_Logit(DEdataframeTime,formula("Surv(SurvTime,status)~."));
            topDecorNames <- names(pDe);
  
            dc <- getLatentCoefficients(DEdataframeTrain)
            deNames_in_dc <- topDecorNames[topDecorNames %in% names(dc)]
            selectedlist <- dc[deNames_in_dc]
            theDeFormulas <- selectedlist
            
            
            Average_Latent_Size <- c(Average_Latent_Size,length(unlist(theDeFormulas))/length(theDeFormulas))
            
            topSigDe <- topDecorNames
            
            pDeDe <- pDe[names(pDe) %in% varlistDe[,1]]
            pDeDe <- pDeDe[pDeDe<sig_pvalue]
            
            La_Significant <- c(La_Significant,length(topSigDe))
            SigDe <- c(SigDe,length(pDeDe))
  
            
            mlKNN <- KNN_method(formula(paste(outcome,"~.")),DEdataframeTrain[,c(outcome,topSigDe)])
            psb <- predictionStats_binary(cbind(dataframeTest[,outcome],
                                                  predict(mlKNN,
                                                  DEdataframe[,c(outcome,topSigDe)])),plotname,cex=0.6)
            pbKNNauc <- c(pbKNNauc,psb$aucs[1])
            pbKNNacc <- c(pbKNNacc,psb$accc[1])
          }
          totBaM <- rbind(totBaM,totBa)
          totDeM <- rbind(totDeM,totDe)
          toUnmatM <- rbind(toUnmatM,toUnmat)
          unalteredM <- rbind(unalteredM,unaltered)
          Decorrleated_FractionM <- rbind(Decorrleated_FractionM,Decorrleated_Fraction)
          Base_FractionM <- rbind(Base_FractionM,Base_Fraction)
          Unaltered_FractionM <- rbind(Unaltered_FractionM,Unaltered_Fraction)
          sparcityM <- rbind(sparcityM,sparcity)
          Average_Latent_SizeM <- rbind(Average_Latent_SizeM,Average_Latent_Size)
          SigDeM <- rbind(SigDeM,SigDe)
          La_SignificantM <- rbind(La_SignificantM,La_Significant)
          pbKNNaucM <- rbind(pbKNNaucM,pbKNNauc)
          pbKNNaccM <- rbind(pbKNNaccM,pbKNNacc)
  
        }
      }
    }
  }
}

[1] “T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.05 [1] “T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.2 [1] “T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.4 [1] “T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.6 [1] “T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.8 [1] “T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.95 [1] “T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.05 [1] “T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.2 [1] “T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.4 [1] “T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.6 [1] “T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.8 [1] “T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.95 [1] “T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.05 [1] “T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.2 [1] “T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.4 [1] “T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.6 [1] “T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.8 [1] “T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.95 [1] “T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.05 [1] “T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.2 [1] “T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.4 [1] “T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.6 [1] “T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.8 [1] “T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.95 [1] “T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.05 [1] “T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.2 [1] “T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.4 [1] “T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.6 [1] “T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.8 [1] “T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.95 [1] “T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.05 [1] “T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.2 [1] “T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.4 [1] “T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.6 [1] “T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.8 [1] “T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.95 [1] “T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.05 [1] “T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.2 [1] “T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.4 [1] “T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.6 [1] “T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.8 [1] “T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.95 [1] “T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.05 [1] “T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.2 [1] “T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.4 [1] “T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.6 [1] “T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.8 [1] “T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.95 [1] “T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.05 [1] “T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.2 [1] “T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.4 [1] “T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.6 [1] “T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.8 [1] “T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.95 [1] “T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.05 [1] “T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.2 [1] “T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.4 [1] “T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.6 [1] “T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.8 [1] “T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.95 [1] “T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.05 [1] “T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.2 [1] “T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.4 [1] “T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.6 [1] “T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.8 [1] “T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.95 [1] “T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.05 [1] “T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.2 [1] “T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.4 [1] “T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.6 [1] “T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.8 [1] “T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.95 [1] “T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.05 [1] “T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.2 [1] “T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.4 [1] “T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.6 [1] “T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.8 [1] “T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.95 [1] “T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.05 [1] “T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.2 [1] “T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.4 [1] “T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.6 [1] “T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.8 [1] “T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.95 [1] “T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.05 [1] “T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.2 [1] “T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.4 [1] “T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.6 [1] “T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.8 [1] “T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.95 [1] “T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.05 [1] “T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.2 [1] “T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.4 [1] “T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.6 [1] “T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.8 [1] “T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.95 [1] “T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.05 [1] “T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.2 [1] “T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.4 [1] “T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.6 [1] “T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.8 [1] “T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.95 [1] “T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.05 [1] “T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.2 [1] “T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.4 [1] “T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.6 [1] “T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.8 [1] “T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.95 [1] “T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.05 [1] “T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.2 [1] “T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.4 [1] “T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.6 [1] “T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.8 [1] “T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.95 [1] “T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.05 [1] “T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.2 [1] “T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.4 [1] “T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.6 [1] “T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.8 [1] “T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.95 [1] “T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.05 [1] “T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.2 [1] “T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.4 [1] “T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.6 [1] “T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.8 [1] “T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.95 [1] “T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.05 [1] “T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.2 [1] “T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.4 [1] “T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.6 [1] “T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.8 [1] “T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.95 [1] “T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.05 [1] “T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.2 [1] “T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.4 [1] “T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.6 [1] “T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.8 [1] “T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.95 [1] “T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.05 [1] “T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.2 [1] “T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.4 [1] “T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.6 [1] “T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.8 [1] “T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.95

par(op)

2.2.1 Printing the analysis outputs

par(op)
par(mfrow=c(1,2),cex=0.6)

rownames(totBaM) <- thenames
rownames(totDeM) <- thenames
rownames(toUnmatM) <- thenames
rownames(unalteredM) <- thenames
rownames(Decorrleated_FractionM) <- thenames
rownames(Base_FractionM) <- thenames
rownames(Unaltered_FractionM) <- thenames
rownames(sparcityM) <- thenames
rownames(Average_Latent_SizeM) <- thenames
rownames(SigDeM) <- thenames
rownames(La_SignificantM) <- thenames
rownames(pbKNNaucM) <- thenames
rownames(pbKNNaccM) <- thenames

colnames(totBaM) <- thr
colnames(totDeM) <- thr
colnames(toUnmatM) <- thr
colnames(unalteredM) <- thr
colnames(Decorrleated_FractionM) <- thr
colnames(Base_FractionM) <- thr
colnames(Unaltered_FractionM) <- thr
colnames(sparcityM) <- thr
colnames(Average_Latent_SizeM) <- thr
colnames(SigDeM) <- thr
colnames(La_SignificantM) <- thr
colnames(pbKNNaucM) <- thr
colnames(pbKNNaccM) <- thr

pander::pander(totFe)

327

pander::pander(totBaM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	4	48	65	69	23	4
T_Blind_fast_LM_TRUE	24	36	48	59	22	4
T_Blind_fast_RLM_FALSE	4	48	65	69	23	4
T_Blind_fast_RLM_TRUE	24	36	48	59	22	4
T_Blind_pearson_LM_FALSE	5	48	65	69	23	4
T_Blind_pearson_LM_TRUE	23	36	48	59	22	4
T_Blind_pearson_RLM_FALSE	50	53	64	69	23	4
T_Blind_pearson_RLM_TRUE	33	36	45	58	22	4
T_Blind_spearman_LM_FALSE	38	45	62	62	22	3
T_Blind_spearman_LM_TRUE	24	44	46	43	22	3
T_Blind_spearman_RLM_FALSE	52	59	60	61	23	3
T_Blind_spearman_RLM_TRUE	37	43	48	45	23	3
T_Driven_fast_LM_FALSE	61	47	66	70	23	4
T_Driven_fast_LM_TRUE	14	36	54	54	21	4
T_Driven_fast_RLM_FALSE	61	47	66	70	23	4
T_Driven_fast_RLM_TRUE	14	36	54	54	21	4
T_Driven_pearson_LM_FALSE	59	46	66	70	23	4
T_Driven_pearson_LM_TRUE	4	36	54	54	21	4
T_Driven_pearson_RLM_FALSE	64	51	62	72	23	4
T_Driven_pearson_RLM_TRUE	43	42	54	55	21	4
T_Driven_spearman_LM_FALSE	55	58	61	62	20	3
T_Driven_spearman_LM_TRUE	37	53	49	47	20	3
T_Driven_spearman_RLM_FALSE	55	54	61	63	21	3
T_Driven_spearman_RLM_TRUE	43	47	51	46	21	3

pander::pander(totDeM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	320	241	180	124	30	4
T_Blind_fast_LM_TRUE	246	255	197	130	33	4
T_Blind_fast_RLM_FALSE	320	241	180	124	30	4
T_Blind_fast_RLM_TRUE	246	255	197	130	33	4
T_Blind_pearson_LM_FALSE	319	241	180	124	30	4
T_Blind_pearson_LM_TRUE	253	255	197	130	33	4
T_Blind_pearson_RLM_FALSE	216	211	179	124	30	4
T_Blind_pearson_RLM_TRUE	226	229	200	130	33	4
T_Blind_spearman_LM_FALSE	209	219	171	113	25	3
T_Blind_spearman_LM_TRUE	231	229	189	122	32	3
T_Blind_spearman_RLM_FALSE	191	197	175	113	25	3
T_Blind_spearman_RLM_TRUE	215	219	187	120	32	3
T_Driven_fast_LM_FALSE	196	240	177	119	32	4
T_Driven_fast_LM_TRUE	298	255	191	127	33	4
T_Driven_fast_RLM_FALSE	196	240	177	119	32	4
T_Driven_fast_RLM_TRUE	298	255	191	127	33	4
T_Driven_pearson_LM_FALSE	198	241	177	119	32	4
T_Driven_pearson_LM_TRUE	321	255	191	127	33	4
T_Driven_pearson_RLM_FALSE	188	214	183	116	32	4
T_Driven_pearson_RLM_TRUE	211	221	192	127	33	4
T_Driven_spearman_LM_FALSE	178	198	174	107	25	3
T_Driven_spearman_LM_TRUE	199	211	183	119	32	3
T_Driven_spearman_RLM_FALSE	189	201	175	107	25	3
T_Driven_spearman_RLM_TRUE	210	213	185	119	32	3

pander::pander(toUnmatM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	4	48	65	69	23	4
T_Blind_fast_LM_TRUE	24	36	48	59	22	4
T_Blind_fast_RLM_FALSE	4	48	65	69	23	4
T_Blind_fast_RLM_TRUE	24	36	48	59	22	4
T_Blind_pearson_LM_FALSE	5	48	65	69	23	4
T_Blind_pearson_LM_TRUE	23	36	48	59	22	4
T_Blind_pearson_RLM_FALSE	50	53	64	69	23	4
T_Blind_pearson_RLM_TRUE	33	36	45	58	22	4
T_Blind_spearman_LM_FALSE	38	45	62	62	22	3
T_Blind_spearman_LM_TRUE	24	44	46	43	22	3
T_Blind_spearman_RLM_FALSE	52	59	60	61	23	3
T_Blind_spearman_RLM_TRUE	37	43	48	45	23	3
T_Driven_fast_LM_FALSE	61	47	66	70	23	4
T_Driven_fast_LM_TRUE	14	36	54	54	21	4
T_Driven_fast_RLM_FALSE	61	47	66	70	23	4
T_Driven_fast_RLM_TRUE	14	36	54	54	21	4
T_Driven_pearson_LM_FALSE	59	46	66	70	23	4
T_Driven_pearson_LM_TRUE	4	36	54	54	21	4
T_Driven_pearson_RLM_FALSE	64	51	62	72	23	4
T_Driven_pearson_RLM_TRUE	43	42	54	55	21	4
T_Driven_spearman_LM_FALSE	55	58	61	62	20	3
T_Driven_spearman_LM_TRUE	37	53	49	47	20	3
T_Driven_spearman_RLM_FALSE	55	54	61	63	21	3
T_Driven_spearman_RLM_TRUE	43	47	51	46	21	3

pander::pander(unalteredM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	7	86	147	203	297	323
T_Blind_fast_LM_TRUE	81	72	130	197	294	323
T_Blind_fast_RLM_FALSE	7	86	147	203	297	323
T_Blind_fast_RLM_TRUE	81	72	130	197	294	323
T_Blind_pearson_LM_FALSE	8	86	147	203	297	323
T_Blind_pearson_LM_TRUE	74	72	130	197	294	323
T_Blind_pearson_RLM_FALSE	111	116	148	203	297	323
T_Blind_pearson_RLM_TRUE	101	98	127	197	294	323
T_Blind_spearman_LM_FALSE	118	108	156	214	302	324
T_Blind_spearman_LM_TRUE	96	98	138	205	295	324
T_Blind_spearman_RLM_FALSE	136	130	152	214	302	324
T_Blind_spearman_RLM_TRUE	112	108	140	207	295	324
T_Driven_fast_LM_FALSE	131	87	150	208	295	323
T_Driven_fast_LM_TRUE	29	72	136	200	294	323
T_Driven_fast_RLM_FALSE	131	87	150	208	295	323
T_Driven_fast_RLM_TRUE	29	72	136	200	294	323
T_Driven_pearson_LM_FALSE	129	86	150	208	295	323
T_Driven_pearson_LM_TRUE	6	72	136	200	294	323
T_Driven_pearson_RLM_FALSE	139	113	144	211	295	323
T_Driven_pearson_RLM_TRUE	116	106	135	200	294	323
T_Driven_spearman_LM_FALSE	149	129	153	220	302	324
T_Driven_spearman_LM_TRUE	128	116	144	208	295	324
T_Driven_spearman_RLM_FALSE	138	126	152	220	302	324
T_Driven_spearman_RLM_TRUE	117	114	142	208	295	324

pander::pander(Decorrleated_FractionM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	0.979	0.737	0.550	0.379	0.0917	0.01223
T_Blind_fast_LM_TRUE	0.752	0.780	0.602	0.398	0.1009	0.01223
T_Blind_fast_RLM_FALSE	0.979	0.737	0.550	0.379	0.0917	0.01223
T_Blind_fast_RLM_TRUE	0.752	0.780	0.602	0.398	0.1009	0.01223
T_Blind_pearson_LM_FALSE	0.976	0.737	0.550	0.379	0.0917	0.01223
T_Blind_pearson_LM_TRUE	0.774	0.780	0.602	0.398	0.1009	0.01223
T_Blind_pearson_RLM_FALSE	0.661	0.645	0.547	0.379	0.0917	0.01223
T_Blind_pearson_RLM_TRUE	0.691	0.700	0.612	0.398	0.1009	0.01223
T_Blind_spearman_LM_FALSE	0.639	0.670	0.523	0.346	0.0765	0.00917
T_Blind_spearman_LM_TRUE	0.706	0.700	0.578	0.373	0.0979	0.00917
T_Blind_spearman_RLM_FALSE	0.584	0.602	0.535	0.346	0.0765	0.00917
T_Blind_spearman_RLM_TRUE	0.657	0.670	0.572	0.367	0.0979	0.00917
T_Driven_fast_LM_FALSE	0.599	0.734	0.541	0.364	0.0979	0.01223
T_Driven_fast_LM_TRUE	0.911	0.780	0.584	0.388	0.1009	0.01223
T_Driven_fast_RLM_FALSE	0.599	0.734	0.541	0.364	0.0979	0.01223
T_Driven_fast_RLM_TRUE	0.911	0.780	0.584	0.388	0.1009	0.01223
T_Driven_pearson_LM_FALSE	0.606	0.737	0.541	0.364	0.0979	0.01223
T_Driven_pearson_LM_TRUE	0.982	0.780	0.584	0.388	0.1009	0.01223
T_Driven_pearson_RLM_FALSE	0.575	0.654	0.560	0.355	0.0979	0.01223
T_Driven_pearson_RLM_TRUE	0.645	0.676	0.587	0.388	0.1009	0.01223
T_Driven_spearman_LM_FALSE	0.544	0.606	0.532	0.327	0.0765	0.00917
T_Driven_spearman_LM_TRUE	0.609	0.645	0.560	0.364	0.0979	0.00917
T_Driven_spearman_RLM_FALSE	0.578	0.615	0.535	0.327	0.0765	0.00917
T_Driven_spearman_RLM_TRUE	0.642	0.651	0.566	0.364	0.0979	0.00917

pander::pander(Base_FractionM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	0.0122	0.147	0.199	0.211	0.0703	0.01223
T_Blind_fast_LM_TRUE	0.0734	0.110	0.147	0.180	0.0673	0.01223
T_Blind_fast_RLM_FALSE	0.0122	0.147	0.199	0.211	0.0703	0.01223
T_Blind_fast_RLM_TRUE	0.0734	0.110	0.147	0.180	0.0673	0.01223
T_Blind_pearson_LM_FALSE	0.0153	0.147	0.199	0.211	0.0703	0.01223
T_Blind_pearson_LM_TRUE	0.0703	0.110	0.147	0.180	0.0673	0.01223
T_Blind_pearson_RLM_FALSE	0.1529	0.162	0.196	0.211	0.0703	0.01223
T_Blind_pearson_RLM_TRUE	0.1009	0.110	0.138	0.177	0.0673	0.01223
T_Blind_spearman_LM_FALSE	0.1162	0.138	0.190	0.190	0.0673	0.00917
T_Blind_spearman_LM_TRUE	0.0734	0.135	0.141	0.131	0.0673	0.00917
T_Blind_spearman_RLM_FALSE	0.1590	0.180	0.183	0.187	0.0703	0.00917
T_Blind_spearman_RLM_TRUE	0.1131	0.131	0.147	0.138	0.0703	0.00917
T_Driven_fast_LM_FALSE	0.1865	0.144	0.202	0.214	0.0703	0.01223
T_Driven_fast_LM_TRUE	0.0428	0.110	0.165	0.165	0.0642	0.01223
T_Driven_fast_RLM_FALSE	0.1865	0.144	0.202	0.214	0.0703	0.01223
T_Driven_fast_RLM_TRUE	0.0428	0.110	0.165	0.165	0.0642	0.01223
T_Driven_pearson_LM_FALSE	0.1804	0.141	0.202	0.214	0.0703	0.01223
T_Driven_pearson_LM_TRUE	0.0122	0.110	0.165	0.165	0.0642	0.01223
T_Driven_pearson_RLM_FALSE	0.1957	0.156	0.190	0.220	0.0703	0.01223
T_Driven_pearson_RLM_TRUE	0.1315	0.128	0.165	0.168	0.0642	0.01223
T_Driven_spearman_LM_FALSE	0.1682	0.177	0.187	0.190	0.0612	0.00917
T_Driven_spearman_LM_TRUE	0.1131	0.162	0.150	0.144	0.0612	0.00917
T_Driven_spearman_RLM_FALSE	0.1682	0.165	0.187	0.193	0.0642	0.00917
T_Driven_spearman_RLM_TRUE	0.1315	0.144	0.156	0.141	0.0642	0.00917

pander::pander(Unaltered_FractionM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	0.0214	0.263	0.450	0.621	0.908	0.988
T_Blind_fast_LM_TRUE	0.2477	0.220	0.398	0.602	0.899	0.988
T_Blind_fast_RLM_FALSE	0.0214	0.263	0.450	0.621	0.908	0.988
T_Blind_fast_RLM_TRUE	0.2477	0.220	0.398	0.602	0.899	0.988
T_Blind_pearson_LM_FALSE	0.0245	0.263	0.450	0.621	0.908	0.988
T_Blind_pearson_LM_TRUE	0.2263	0.220	0.398	0.602	0.899	0.988
T_Blind_pearson_RLM_FALSE	0.3394	0.355	0.453	0.621	0.908	0.988
T_Blind_pearson_RLM_TRUE	0.3089	0.300	0.388	0.602	0.899	0.988
T_Blind_spearman_LM_FALSE	0.3609	0.330	0.477	0.654	0.924	0.991
T_Blind_spearman_LM_TRUE	0.2936	0.300	0.422	0.627	0.902	0.991
T_Blind_spearman_RLM_FALSE	0.4159	0.398	0.465	0.654	0.924	0.991
T_Blind_spearman_RLM_TRUE	0.3425	0.330	0.428	0.633	0.902	0.991
T_Driven_fast_LM_FALSE	0.4006	0.266	0.459	0.636	0.902	0.988
T_Driven_fast_LM_TRUE	0.0887	0.220	0.416	0.612	0.899	0.988
T_Driven_fast_RLM_FALSE	0.4006	0.266	0.459	0.636	0.902	0.988
T_Driven_fast_RLM_TRUE	0.0887	0.220	0.416	0.612	0.899	0.988
T_Driven_pearson_LM_FALSE	0.3945	0.263	0.459	0.636	0.902	0.988
T_Driven_pearson_LM_TRUE	0.0183	0.220	0.416	0.612	0.899	0.988
T_Driven_pearson_RLM_FALSE	0.4251	0.346	0.440	0.645	0.902	0.988
T_Driven_pearson_RLM_TRUE	0.3547	0.324	0.413	0.612	0.899	0.988
T_Driven_spearman_LM_FALSE	0.4557	0.394	0.468	0.673	0.924	0.991
T_Driven_spearman_LM_TRUE	0.3914	0.355	0.440	0.636	0.902	0.991
T_Driven_spearman_RLM_FALSE	0.4220	0.385	0.465	0.673	0.924	0.991
T_Driven_spearman_RLM_TRUE	0.3578	0.349	0.434	0.636	0.902	0.991

pander::pander(sparcityM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	0.34368	0.04076	0.00666	0.00465	0.00338	0.00310
T_Blind_fast_LM_TRUE	0.01914	0.06419	0.00795	0.00483	0.00340	0.00310
T_Blind_fast_RLM_FALSE	0.34368	0.04076	0.00666	0.00465	0.00338	0.00310
T_Blind_fast_RLM_TRUE	0.01914	0.06419	0.00795	0.00483	0.00340	0.00310
T_Blind_pearson_LM_FALSE	0.36673	0.04076	0.00666	0.00465	0.00338	0.00310
T_Blind_pearson_LM_TRUE	0.02012	0.06419	0.00795	0.00483	0.00340	0.00310
T_Blind_pearson_RLM_FALSE	0.01009	0.01056	0.00706	0.00468	0.00338	0.00310
T_Blind_pearson_RLM_TRUE	0.00899	0.01011	0.00837	0.00483	0.00340	0.00310
T_Blind_spearman_LM_FALSE	0.00790	0.03341	0.00696	0.00450	0.00334	0.00309
T_Blind_spearman_LM_TRUE	0.01390	0.03865	0.00835	0.00462	0.00341	0.00309
T_Blind_spearman_RLM_FALSE	0.00775	0.00867	0.00738	0.00450	0.00335	0.00309
T_Blind_spearman_RLM_TRUE	0.01076	0.01001	0.00772	0.00467	0.00342	0.00309
T_Driven_fast_LM_FALSE	0.00798	0.04196	0.00689	0.00454	0.00339	0.00310
T_Driven_fast_LM_TRUE	0.07902	0.06023	0.00778	0.00452	0.00339	0.00310
T_Driven_fast_RLM_FALSE	0.00798	0.04196	0.00689	0.00454	0.00339	0.00310
T_Driven_fast_RLM_TRUE	0.07902	0.06023	0.00778	0.00452	0.00339	0.00310
T_Driven_pearson_LM_FALSE	0.00822	0.04234	0.00689	0.00454	0.00339	0.00310
T_Driven_pearson_LM_TRUE	0.19312	0.06023	0.00778	0.00452	0.00339	0.00310
T_Driven_pearson_RLM_FALSE	0.00722	0.00916	0.00714	0.00450	0.00339	0.00310
T_Driven_pearson_RLM_TRUE	0.00835	0.01034	0.00774	0.00456	0.00339	0.00310
T_Driven_spearman_LM_FALSE	0.00659	0.00772	0.00633	0.00437	0.00331	0.00309
T_Driven_spearman_LM_TRUE	0.00823	0.00852	0.00812	0.00446	0.00339	0.00309
T_Driven_spearman_RLM_FALSE	0.00762	0.00850	0.00685	0.00435	0.00332	0.00309
T_Driven_spearman_RLM_TRUE	0.00926	0.00860	0.00777	0.00445	0.00339	0.00309

pander::pander(Average_Latent_SizeM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	121.85	19.74	2.84	2.33	2.09	2
T_Blind_fast_LM_TRUE	6.78	29.04	3.39	2.55	2.09	2
T_Blind_fast_RLM_FALSE	121.85	19.74	2.84	2.33	2.09	2
T_Blind_fast_RLM_TRUE	6.78	29.04	3.39	2.55	2.09	2
T_Blind_pearson_LM_FALSE	115.17	19.74	2.84	2.33	2.09	2
T_Blind_pearson_LM_TRUE	6.83	29.04	3.39	2.55	2.09	2
T_Blind_pearson_RLM_FALSE	5.07	5.94	3.72	2.36	2.10	2
T_Blind_pearson_RLM_TRUE	3.31	4.45	3.94	2.55	2.08	2
T_Blind_spearman_LM_FALSE	3.52	13.94	4.16	2.39	2.18	2
T_Blind_spearman_LM_TRUE	6.19	14.27	3.85	2.39	2.25	2
T_Blind_spearman_RLM_FALSE	3.98	4.77	3.71	2.41	2.30	2
T_Blind_spearman_RLM_TRUE	4.09	3.57	3.76	2.45	2.31	2
T_Driven_fast_LM_FALSE	4.77	18.68	3.76	2.34	2.09	2
T_Driven_fast_LM_TRUE	22.22	29.11	4.09	2.14	2.09	2
T_Driven_fast_RLM_FALSE	4.77	18.68	3.76	2.34	2.09	2
T_Driven_fast_RLM_TRUE	22.22	29.11	4.09	2.14	2.09	2
T_Driven_pearson_LM_FALSE	5.39	19.91	3.76	2.34	2.09	2
T_Driven_pearson_LM_TRUE	67.38	29.11	4.09	2.14	2.09	2
T_Driven_pearson_RLM_FALSE	3.55	4.65	3.62	2.42	2.09	2
T_Driven_pearson_RLM_TRUE	3.78	5.17	3.67	2.14	2.08	2
T_Driven_spearman_LM_FALSE	3.00	4.58	3.21	2.33	2.12	NA
T_Driven_spearman_LM_TRUE	3.72	3.69	3.62	2.27	2.17	NA
T_Driven_spearman_RLM_FALSE	4.50	5.25	3.63	2.38	2.25	2
T_Driven_spearman_RLM_TRUE	3.83	3.74	3.37	2.30	2.23	2

pander::pander(SigDeM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	6	7	9	11	2	1
T_Blind_fast_LM_TRUE	6	6	7	18	7	1
T_Blind_fast_RLM_FALSE	6	7	9	11	2	1
T_Blind_fast_RLM_TRUE	6	6	7	18	7	1
T_Blind_pearson_LM_FALSE	6	7	9	11	2	1
T_Blind_pearson_LM_TRUE	6	6	7	18	7	1
T_Blind_pearson_RLM_FALSE	10	10	12	11	2	1
T_Blind_pearson_RLM_TRUE	13	9	11	23	7	1
T_Blind_spearman_LM_FALSE	11	7	12	11	3	0
T_Blind_spearman_LM_TRUE	8	3	12	24	3	0
T_Blind_spearman_RLM_FALSE	10	11	8	11	3	0
T_Blind_spearman_RLM_TRUE	10	13	9	24	3	0
T_Driven_fast_LM_FALSE	2	2	8	7	0	1
T_Driven_fast_LM_TRUE	1	2	10	9	5	1
T_Driven_fast_RLM_FALSE	2	2	8	7	0	1
T_Driven_fast_RLM_TRUE	1	2	10	9	5	1
T_Driven_pearson_LM_FALSE	1	2	8	7	0	1
T_Driven_pearson_LM_TRUE	1	2	10	9	5	1
T_Driven_pearson_RLM_FALSE	7	7	10	9	0	0
T_Driven_pearson_RLM_TRUE	7	6	11	10	4	0
T_Driven_spearman_LM_FALSE	2	2	5	7	1	0
T_Driven_spearman_LM_TRUE	2	1	5	11	1	0
T_Driven_spearman_RLM_FALSE	12	7	7	6	0	0
T_Driven_spearman_RLM_TRUE	5	5	6	11	1	0

pander::pander(La_SignificantM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	20	31	61	90	143	157
T_Blind_fast_LM_TRUE	29	30	61	101	139	157
T_Blind_fast_RLM_FALSE	20	31	61	90	143	157
T_Blind_fast_RLM_TRUE	29	30	61	101	139	157
T_Blind_pearson_LM_FALSE	18	31	61	90	143	157
T_Blind_pearson_LM_TRUE	30	30	61	101	139	157
T_Blind_pearson_RLM_FALSE	71	69	82	88	142	157
T_Blind_pearson_RLM_TRUE	47	54	67	101	141	157
T_Blind_spearman_LM_FALSE	53	45	74	92	143	157
T_Blind_spearman_LM_TRUE	29	34	59	117	135	157
T_Blind_spearman_RLM_FALSE	65	65	65	93	142	157
T_Blind_spearman_RLM_TRUE	43	49	71	116	136	157
T_Driven_fast_LM_FALSE	32	32	60	107	142	156
T_Driven_fast_LM_TRUE	10	25	69	104	141	156
T_Driven_fast_RLM_FALSE	32	32	60	107	142	156
T_Driven_fast_RLM_TRUE	10	25	69	104	141	156
T_Driven_pearson_LM_FALSE	26	32	60	107	142	156
T_Driven_pearson_LM_TRUE	9	25	69	104	141	156
T_Driven_pearson_RLM_FALSE	61	59	68	116	142	157
T_Driven_pearson_RLM_TRUE	51	52	63	107	142	157
T_Driven_spearman_LM_FALSE	13	65	65	107	141	156
T_Driven_spearman_LM_TRUE	26	37	67	113	136	156
T_Driven_spearman_RLM_FALSE	57	55	60	113	141	157
T_Driven_spearman_RLM_TRUE	39	44	64	119	137	157

pander::pander(pbKNNaucM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	0.728	0.727	0.768	0.772	0.773	0.771
T_Blind_fast_LM_TRUE	0.743	0.744	0.744	0.768	0.774	0.771
T_Blind_fast_RLM_FALSE	0.728	0.727	0.768	0.772	0.773	0.771
T_Blind_fast_RLM_TRUE	0.743	0.744	0.744	0.768	0.774	0.771
T_Blind_pearson_LM_FALSE	0.751	0.727	0.768	0.772	0.773	0.771
T_Blind_pearson_LM_TRUE	0.744	0.744	0.744	0.768	0.774	0.771
T_Blind_pearson_RLM_FALSE	0.747	0.753	0.776	0.768	0.774	0.769
T_Blind_pearson_RLM_TRUE	0.737	0.743	0.736	0.772	0.763	0.769
T_Blind_spearman_LM_FALSE	0.765	0.775	0.771	0.773	0.783	0.772
T_Blind_spearman_LM_TRUE	0.745	0.755	0.774	0.756	0.783	0.772
T_Blind_spearman_RLM_FALSE	0.743	0.754	0.779	0.749	0.788	0.774
T_Blind_spearman_RLM_TRUE	0.783	0.759	0.780	0.766	0.774	0.774
T_Driven_fast_LM_FALSE	0.754	0.725	0.757	0.772	0.786	0.764
T_Driven_fast_LM_TRUE	0.707	0.723	0.731	0.779	0.769	0.764
T_Driven_fast_RLM_FALSE	0.754	0.725	0.757	0.772	0.786	0.764
T_Driven_fast_RLM_TRUE	0.707	0.723	0.731	0.779	0.769	0.764
T_Driven_pearson_LM_FALSE	0.777	0.723	0.757	0.772	0.786	0.764
T_Driven_pearson_LM_TRUE	0.715	0.723	0.731	0.779	0.769	0.764
T_Driven_pearson_RLM_FALSE	0.785	0.765	0.770	0.759	0.781	0.769
T_Driven_pearson_RLM_TRUE	0.741	0.750	0.776	0.758	0.768	0.769
T_Driven_spearman_LM_FALSE	0.764	0.740	0.766	0.779	0.782	0.771
T_Driven_spearman_LM_TRUE	0.747	0.766	0.747	0.764	0.780	0.771
T_Driven_spearman_RLM_FALSE	0.759	0.738	0.783	0.770	0.783	0.773
T_Driven_spearman_RLM_TRUE	0.756	0.784	0.758	0.761	0.769	0.773

pander::pander(pbKNNaccM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	0.674	0.706	0.715	0.694	0.697	0.685
T_Blind_fast_LM_TRUE	0.7	0.709	0.682	0.7	0.709	0.685
T_Blind_fast_RLM_FALSE	0.674	0.706	0.715	0.694	0.697	0.685
T_Blind_fast_RLM_TRUE	0.7	0.709	0.682	0.7	0.709	0.685
T_Blind_pearson_LM_FALSE	0.697	0.706	0.715	0.694	0.697	0.685
T_Blind_pearson_LM_TRUE	0.7	0.709	0.682	0.7	0.709	0.685
T_Blind_pearson_RLM_FALSE	0.709	0.712	0.718	0.694	0.694	0.691
T_Blind_pearson_RLM_TRUE	0.688	0.7	0.697	0.715	0.697	0.691
T_Blind_spearman_LM_FALSE	0.7	0.721	0.703	0.724	0.724	0.694
T_Blind_spearman_LM_TRUE	0.706	0.721	0.712	0.694	0.724	0.694
T_Blind_spearman_RLM_FALSE	0.697	0.671	0.724	0.697	0.721	0.697
T_Blind_spearman_RLM_TRUE	0.724	0.691	0.718	0.709	0.726	0.697
T_Driven_fast_LM_FALSE	0.691	0.682	0.703	0.703	0.712	0.697
T_Driven_fast_LM_TRUE	0.718	0.718	0.688	0.706	0.709	0.697
T_Driven_fast_RLM_FALSE	0.691	0.682	0.703	0.703	0.712	0.697
T_Driven_fast_RLM_TRUE	0.718	0.718	0.688	0.706	0.709	0.697
T_Driven_pearson_LM_FALSE	0.694	0.668	0.703	0.703	0.712	0.697
T_Driven_pearson_LM_TRUE	0.694	0.718	0.688	0.706	0.709	0.697
T_Driven_pearson_RLM_FALSE	0.691	0.7	0.712	0.7	0.7	0.688
T_Driven_pearson_RLM_TRUE	0.706	0.712	0.718	0.703	0.7	0.688
T_Driven_spearman_LM_FALSE	0.703	0.703	0.703	0.718	0.715	0.706
T_Driven_spearman_LM_TRUE	0.7	0.712	0.694	0.703	0.729	0.706
T_Driven_spearman_RLM_FALSE	0.668	0.709	0.724	0.682	0.709	0.697
T_Driven_spearman_RLM_TRUE	0.691	0.676	0.7	0.697	0.724	0.697

miny = min(pbKNNaucM)-0.05
maxy = max(pbKNNaucM)+0.15

plot(thr,pbKNNaucM[1,],ylim=c(miny,maxy),
     main="KNN's ROCAUC",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="ROC AUC",
     type="l",
     col=1,
     lwd=2)
for (ind in 2:nrow(pbKNNaucM))
{
  lines(thr,pbKNNaucM[ind,],col=ind,lwd=2,lty=ind)
}

legend("topleft", rownames(pbKNNaucM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)

fastRows <- str_detect(rownames(pbKNNaucM),"fast")
pearsonRows <- str_detect(rownames(pbKNNaucM),"pearson")
spearmanRows <- str_detect(rownames(pbKNNaucM),"spearman")
T_BlindRows <- str_detect(rownames(pbKNNaucM),"T_Blind")
corRankRows <- str_detect(rownames(pbKNNaucM),"TRUE")
maxCorRankRows <- str_detect(rownames(pbKNNaucM),"FALSE")
RLMfitMethod <- str_detect(rownames(pbKNNaucM),"RLM")


meanAuc <-  colMeans(pbKNNaucM[fastRows,])
meanAuc <-  rbind(meanAuc,colMeans(pbKNNaucM[pearsonRows,]))
meanAuc <-  rbind(meanAuc,colMeans(pbKNNaucM[spearmanRows,]))
meanAuc <-  rbind(meanAuc,colMeans(pbKNNaucM[!T_BlindRows,]))
meanAuc <-  rbind(meanAuc,colMeans(pbKNNaucM[T_BlindRows,]))
meanAuc <-  rbind(meanAuc,colMeans(pbKNNaucM[corRankRows,]))
meanAuc <-  rbind(meanAuc,colMeans(pbKNNaucM[maxCorRankRows,]))
meanAuc <-  rbind(meanAuc,colMeans(pbKNNaucM[RLMfitMethod,]))
meanAuc <-  rbind(meanAuc,colMeans(pbKNNaucM[!RLMfitMethod,]))
legnames <- c("fast","Pearson","Spearman","T_Driven","T_Blind","SumCor","MaxCor","RLM","LM")


pbKNNaccM <- as.data.frame(pbKNNaccM)
pbKNNaccM[,1:ncol(pbKNNaccM)] <- sapply(pbKNNaccM,as.numeric)

Average_Latent_SizeM <- as.data.frame(Average_Latent_SizeM)
Average_Latent_SizeM[,1:ncol(Average_Latent_SizeM)] <- sapply(Average_Latent_SizeM,as.numeric)
Average_Latent_SizeM[is.na(Average_Latent_SizeM)] <- 0

SigDeM <- as.data.frame(SigDeM)
SigDeM[,1:ncol(SigDeM)] <- sapply(SigDeM,as.numeric)

sparcityM <- as.data.frame(sparcityM)
sparcityM[,1:ncol(sparcityM)] <- sapply(sparcityM,as.numeric)

miny = min(meanAuc)-0.01
maxy = max(meanAuc)+0.025

plot(thr,meanAuc[1,],ylim=c(miny,maxy),
     main="Mean KNN's ROCAUC",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="ROC AUC",
     type="l",
     col=1,
     lwd=2,
     lty=1)
for (ind in 2:nrow(meanAuc))
{
  lines(thr,meanAuc[ind,],col=ind,lwd=2,lty=ind)
}
legend("topleft", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

miny = min(pbKNNaccM) - 0.025
maxy = max(pbKNNaccM) + 0.1

plot(thr,pbKNNaccM[1,],ylim=c(miny,maxy),
     main="KNN's Accuracy",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="Accuracy",
     type="l",
     col=1,
     lwd=2)
for (ind in 2:nrow(pbKNNaucM))
{
  lines(thr,pbKNNaccM[ind,],col=ind,lwd=2,lty=ind)
}
legend("topleft", rownames(pbKNNaucM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)



meanAcc <-  colMeans(pbKNNaccM[fastRows,])
meanAcc <-  rbind(meanAcc,colMeans(pbKNNaccM[pearsonRows,]))
meanAcc <-  rbind(meanAcc,colMeans(pbKNNaccM[spearmanRows,]))
meanAcc <-  rbind(meanAcc,colMeans(pbKNNaccM[!T_BlindRows,]))
meanAcc <-  rbind(meanAcc,colMeans(pbKNNaccM[T_BlindRows,]))
meanAcc <-  rbind(meanAcc,colMeans(pbKNNaccM[corRankRows,]))
meanAcc <-  rbind(meanAcc,colMeans(pbKNNaccM[maxCorRankRows,]))
meanAcc <-  rbind(meanAcc,colMeans(pbKNNaccM[RLMfitMethod,]))
meanAcc <-  rbind(meanAcc,colMeans(pbKNNaccM[!RLMfitMethod,]))

miny = min(meanAcc)-0.01
maxy = max(meanAcc)+0.025

plot(thr,meanAcc[1,],ylim=c(miny,maxy),
     main="Mean KNN's Accuracy",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="Accuracy",
     type="l",
     col=1,
     lwd=2)
for (ind in 2:nrow(meanAcc))
{
  lines(thr,meanAcc[ind,],col=ind,lwd=2,lty=ind)
}
legend("topleft", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

miny = 1
maxy = max(Average_Latent_SizeM)

plot(thr,Average_Latent_SizeM[1,],ylim=c(miny,maxy),
     main="Average Size of Latent-Variable",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="Size",
     type="l",
     col=1,
     lwd=2,
     log = "y")

for (ind in 2:nrow(Average_Latent_SizeM))
{
  lines(thr,Average_Latent_SizeM[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", rownames(Average_Latent_SizeM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)




meanAccAvgSize <-  colMeans(Average_Latent_SizeM[fastRows,])
meanAccAvgSize <-  rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[pearsonRows,]))
meanAccAvgSize <-  rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[spearmanRows,]))
meanAccAvgSize <-  rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[!T_BlindRows,]))
meanAccAvgSize <-  rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[T_BlindRows,]))
meanAccAvgSize <-  rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[corRankRows,]))
meanAccAvgSize <-  rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[maxCorRankRows,]))
meanAccAvgSize <-  rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[RLMfitMethod,]))
meanAccAvgSize <-  rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[!RLMfitMethod,]))

miny =1
maxy = max(meanAccAvgSize) + 10

plot(thr,meanAccAvgSize[1,],ylim=c(miny,maxy),
     main="Mean Size of Average-Latent-Variable",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="Size",
     type="l",
     col=1,
     lwd=2,
     log = "y")
for (ind in 2:nrow(meanAccAvgSize))
{
  lines(thr,meanAccAvgSize[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

miny = min(La_SignificantM)
maxy = max(La_SignificantM)

plot(thr,La_SignificantM[1,],ylim=c(miny,maxy),
     main="Number of Discovered Features",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="Number of Features",
     type="l",
     col=1,
     lwd=2,
     log = "y")

for (ind in 2:nrow(La_SignificantM))
{
  lines(thr,La_SignificantM[ind,],col=ind,lwd=2,lty=ind)
}
legend("bottomright", rownames(La_SignificantM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)




meanDiscovered <-  colMeans(La_SignificantM[fastRows,])
meanDiscovered <-  rbind(meanDiscovered,colMeans(La_SignificantM[pearsonRows,]))
meanDiscovered <-  rbind(meanDiscovered,colMeans(La_SignificantM[spearmanRows,]))
meanDiscovered <-  rbind(meanDiscovered,colMeans(La_SignificantM[!T_BlindRows,]))
meanDiscovered <-  rbind(meanDiscovered,colMeans(La_SignificantM[T_BlindRows,]))
meanDiscovered <-  rbind(meanDiscovered,colMeans(La_SignificantM[corRankRows,]))
meanDiscovered <-  rbind(meanDiscovered,colMeans(La_SignificantM[maxCorRankRows,]))
meanDiscovered <-  rbind(meanDiscovered,colMeans(La_SignificantM[RLMfitMethod,]))
meanDiscovered <-  rbind(meanDiscovered,colMeans(La_SignificantM[!RLMfitMethod,]))

miny = min(meanDiscovered)
maxy = max(meanDiscovered) + 10

plot(thr,meanDiscovered[1,],ylim=c(miny,maxy),
     main="Average Number of Discovered Features",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="Number of Features",
     type="l",
     col=1,
     lwd=2,
     log = "y")
for (ind in 2:nrow(meanDiscovered))
{
  lines(thr,meanDiscovered[ind,],col=ind,lwd=2,lty=ind)
}
legend("bottomright", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

SigDeM[is.na(SigDeM)] <- 0
miny = 1
maxy = max(SigDeM) + 200

plot(thr,SigDeM[1,],ylim=c(miny,maxy),
     main="Number of Significant Latent Variables",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="How Many",
     type="l",
     col=1,
     lwd=2,
     log = "y")

for (ind in 2:nrow(SigDeM))
{
  lines(thr,SigDeM[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", rownames(SigDeM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)



SigLatent <-  colMeans(SigDeM[fastRows,])
SigLatent <-  rbind(SigLatent,colMeans(SigDeM[pearsonRows,]))
SigLatent <-  rbind(SigLatent,colMeans(SigDeM[spearmanRows,]))
SigLatent <-  rbind(SigLatent,colMeans(SigDeM[!T_BlindRows,]))
SigLatent <-  rbind(SigLatent,colMeans(SigDeM[T_BlindRows,]))
SigLatent <-  rbind(SigLatent,colMeans(SigDeM[corRankRows,]))
SigLatent <-  rbind(SigLatent,colMeans(SigDeM[maxCorRankRows,]))
SigLatent <-  rbind(SigLatent,colMeans(SigDeM[RLMfitMethod,]))
SigLatent <-  rbind(SigLatent,colMeans(SigDeM[!RLMfitMethod,]))

miny = 1
maxy = max(SigLatent) + 10


plot(thr,SigLatent[1,],ylim=c(miny,maxy),
     main="Average # of Significant Latent Variables",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="How Many",
     type="l",
     col=1,
     lwd=2,
     log = "y")
for (ind in 2:nrow(SigLatent))
{
  lines(thr,SigLatent[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

sparcityM[is.na(sparcityM)] <- 0
miny = min(sparcityM)
maxy = max(sparcityM) + 0.25 

plot(thr,sparcityM[1,],ylim=c(miny,maxy),
     main="Matrix Sparcity",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="Sparcity",
     type="l",
     col=1,
     lwd=2,
     log = "y")

for (ind in 2:nrow(sparcityM))
{
  lines(thr,sparcityM[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", rownames(sparcityM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)




meanSparcity <-  colMeans(sparcityM[fastRows,])
meanSparcity <-  rbind(meanSparcity,colMeans(sparcityM[pearsonRows,]))
meanSparcity <-  rbind(meanSparcity,colMeans(sparcityM[spearmanRows,]))
meanSparcity <-  rbind(meanSparcity,colMeans(sparcityM[!T_BlindRows,]))
meanSparcity <-  rbind(meanSparcity,colMeans(sparcityM[T_BlindRows,]))
meanSparcity <-  rbind(meanSparcity,colMeans(sparcityM[corRankRows,]))
meanSparcity <-  rbind(meanSparcity,colMeans(sparcityM[maxCorRankRows,]))
meanSparcity <-  rbind(meanSparcity,colMeans(sparcityM[RLMfitMethod,]))
meanSparcity <-  rbind(meanSparcity,colMeans(sparcityM[!RLMfitMethod,]))

miny = min(meanSparcity)
maxy = max(meanSparcity)+0.25


plot(thr,meanSparcity[1,],ylim=c(miny,maxy),
     main="Mean Matrix Sparcity",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="Sparcity",
     type="l",
     col=1,
     lwd=2,
     log = "y")
for (ind in 2:nrow(meanSparcity))
{
  lines(thr,meanSparcity[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

2.2.2 Saving All

save.image("~/GitHub/LatentBiomarkers/RMD/TADPOLE_ALL_Options.RData")